Objective methods for assessing perceptual image quality commonly examine the visibility of the errors. An error is the difference between a distorted image and a reference image. Bearing in mind a variety of known properties of the human visual system, an objective quality metric is able to give a relevant quality value regarding the ground truth. Classical methods used to assess the quality of a distorted signal with respect to a reference signal comprise the following steps depicted on FIG. 1:                A preprocessing step 10: this step consists, for example, in applying a Gamma law on the reference and distorted images to eliminate non linearities of displays;        A contrast sensitivity function (CSF) filtering step 11: CSF deals with the fact that a viewer is not able to perceive all the details in his visual field with the same accuracy; for example, his sensitivity is higher for the horizontal and vertical structures.        a channel decomposition 12 step: in order to simulate the different populations of visual cells (cones, . . . ), images are decomposed into several subbands; each subband may be regarded as the neural image generated by a particular population of visual cells tuned to both a particular orientation and a particular frequency.        An error normalization and masking step 13: these steps deal with the modulation of the sensitivity of the eyes regarding the content of the image; for example, a coding artifact is more visible in a flat region (featured by a weak masking capability) than in a highly textured region (featured by a strong masking capability).        An error pooling step (14): such a step combines the error signals coming from different modalities into a single quality/distortion value.These approaches have a high computational complexity. Furthermore it is difficult to embed such algorithms in a video coding scheme.        